SoroushVahidi/scidocs huggingface dataset:
Probabilistic models in noisy environments : and their application to a visual prosthesis for the blind/
(2005)

In recent years, probabilistic models have become fundamental techniques in machine learning. They are successfully applied in various engineering problems, such as robotics, biometrics, brain-computer interfaces or artificial vision, and will gain in importance in the near future. This work deals with the difficult, but common situation where the data is, either very noisy, or scarce compared to the complexity of the process to model. We focus on latent variable models, which can be formalized as probabilistic graphical models and learned by the expectation-maximization algorithm or its variants (e.g., variational Bayes). After having carefully studied a non-exhaustive list of multivariate kernel density estimators, we established that in most applications locally adaptive estimators should be preferred. Unfortunately, these methods are usually sensitive to outliers and have often too many parameters to set. Therefore, we focus on finite mixture models, which do not suffer from these drawbacks provided some structural modifications. Two questions are central in this dissertation: (i) how to make mixture models robust to noise, i.e. deal efficiently with outliers, and (ii) how to exploit side-channel information, i.e. additional information intrinsic to the data. In order to tackle the first question, we extend the training algorithms of the popular Gaussian mixture models to the Student-t mixture models. The Student-t distribution can be viewed as a heavy-tailed alternative to the Gaussian distribution, the robustness being tuned by an extra parameter, the degrees of freedom. Furthermore, we introduce a new variational Bayesian algorithm for learning Bayesian Student-t mixture models. This algorithm leads to very robust density estimators and clustering. To address the second question, we introduce manifold constrained mixture models. This new technique exploits the information that the data are living on a manifold of lower dimension than the dimension of the feature space. Taking the implicit geometrical data arrangement into account results in better generalization on unseen data. Finally, we show that the latent variable framework used for learning mixture models can be extended to construct probabilistic regularization networks, such as the Relevance Vector Machines. Subsequently, we make use of these methods in the context of an optic nerve visual prosthesis to restore partial vision to blind people of whom the optic nerve is still functional. Although visual sensations
Dual-Band Wide-Angle Scanning Planar Phased Array in X/Ku-Bands
(2014)

A novel planar dual-band phased array, operational in the X/Ku-bands and with wide-angle scanning capability is presented. The design, development and experimental demonstration are described. A new single-layer crossed L-bar microstrip antenna is used for the array design. The antenna has low-profile architecture, measuring only 0.33λ×0.33λ, at the low frequency band of operation, with flexible resonance tuning capability offered by the use of a plate-through-hole and field-matching ring arrangement. A 49-element planar (7 × 7) array demonstrator has been built and its performance validated, exhibiting good agreement with full-wave simulations. The dual-band array supports a large frequency ratio of nearly 1.8:1, and also maintains good sub-band bandwidths. Wide-angle scanning up to a maximum of 60 ° and 50 ° are achieved at the low and high frequency bands of operation, respectively.
Performance Optimization of Aho-Corasick Algorithm on a GPU
(2013)

Aho-Corasick (AC) algorithm is a multiple patternsmatching algorithm commonly used for applications such as computer and network security, bioinformatics, image processing, among others. These applications are computationally demanding, thus optimizing performance for AC algorithm is crucial. In this paper, we present a performance optimization strategy for the AC algorithm on a Graphic Processing Unit(GPU). Our strategy efficiently utilizes the high degree of the on chip parallelism and the complicated memory hierarchy of the GPU so that the aggregate performance (or throughput) for the AC algorithm can be optimized. The strategy significantly cuts down the effective memory access latencies and efficiently utilizes the memory bandwidth. Also, it maximizes the effects of the multithreading capability of the GPU through optimal thread scheduling. Experimental results on Nvidia GeForce GTX 285 GPU show that our approach delivers up to 127 Gbps throughput performance and 222-times speedup compared with a serial version running on single core of 2.2Ghz Core2Duo Intel processor.
Robust acoustic echo cancellation in the short-time fourier transform domain using adaptive crossband filters
(2014)

This paper presents a robust acoustic echo cancellation (AEC) system in the short-time Fourier transform (STFT) domain using adaptive crossband filters. The STFT-domain AEC allows for a simpler system structure compared to the traditional frequency-domain AEC, which normally requires several applications of the discrete Fourier transform (DFT) and the inverse DFT, while the robust AEC (RAEC) allows for continuous and stable filter updates during double talk without freezing the adaptive filter. The RAEC and the STFT-domain AEC have been investigated in the past in separate studies. In this work we propose a novel algorithm that combines the advantages of both approaches for robust update of the adaptive crossband filters even during double talk. Experimental results confirm the benefit of incorporating the robustness constraint for the adaptive crossband filters and show improved performance in terms of the echo reduction and the predicted sound quality.
Sparsity and smoothness via the fused lasso
(2004)

The lasso penalizes a least squares regression by the sum of the absolute values (L1-norm) of the coefficients. The form of this penalty encourages sparse solutions (with many coefficients equal to 0).We propose the ‘fused lasso’, a generalization that is designed for problems with features that can be ordered in some meaningful way. The fused lasso penalizes the L1-norm of both the coefficients and their successive differences. Thus it encourages sparsity of the coefficients and also sparsity of their differences—i.e. local constancy of the coefficient profile.The fused lasso is especially useful when the number of features p is much greater than N , the sample size.The technique is also extended to the ‘hinge’ loss function that underlies the support vector classifier.We illustrate the methods on examples from protein mass spectroscopy and gene expression data.
Efficient and Scalable IoT Service Delivery on Cloud
(2013)

Nowadays, IoT services are typically delivered as physically isolated vertical solutions, in which all system components ranging from sensory devices to applications are customized and tightly coupled for the requirements of each specific project. The efficiency and scalability of such service delivery model are intrinsically limited, posing significant challenges to IoT solution providers. Therefore, we propose a novel PaaS framework that provides essential platform services for IoT solution providers to efficiently deliver and continuously extend their services. This paper first introduces the IoT PaaS architecture, on which IoT solutions can be delivered as virtual verticals by leveraging computing resources and middleware services on cloud. Then we present the detailed mechanism and implementation of domain mediation, which helps solution providers to efficiently provide domain-specific control applications. The proposed approaches are demonstrated through the implementation of a domain mediator for building management and two use cases using the mediator.
Particle Systems - a Technique for Modeling a Class of Fuzzy Objects
(1983)

This paper introduces particle systems--a method for modeling fuzzy objects such as fire, clouds, and water. Particle systems model an object as a cloud of primitive particles that define its volume. Over a period of time, particles are generated into the system, move and change form within the system, and die from the system. The resulting model is able to represent motion, changes of form, and dynamics that are not possible with classical surface-based representations. The particles can easily be motion blurred, and therefore do not exhibit temporal aliasing or strobing. Stochastic processes are used to generate and control the many particles within a particle system. The application of particle systems to the wall of fire element from the Genesis Demo sequence of the film Star Trek II: The Wrath of Khan [10] is presented.
An image synthesizer
(1985)

We introduce the concept of a Pixel Stream Editor. This forms the basis for an interactive synthesizer for designing highly realistic Computer Generated Imagery. The designer works in an interactive Very High Level programming environment which provides a very fast concept/implement/view iteration cycle.Naturalistic visual complexity is built up by composition of non-linear functions, as opposed to the more conventional texture mapping or growth model algorithms. Powerful primitives are included for creating controlled stochastic effects. We introduce the concept of "solid texture" to the field of CGI.We have used this system to create very convincing representations of clouds, fire, water, stars, marble, wood, rock, soap films and crystal. The algorithms created with this paradigm are generally extremely fast, highly realistic, and asynchronously parallelizable at the pixel level.
The Value Function Polytope in Reinforcement Learning
(2019)

We establish geometric and topological properties of the space of value functions in finite stateaction Markov decision processes. Our main contribution is the characterization of the nature of its shape: a general polytope (Aigner et al., 2010). To demonstrate this result, we exhibit several properties of the structural relationship between policies and value functions including the line theorem, which shows that the value functions of policies constrained on all but one state describe a line segment. Finally, we use this novel perspective to introduce visualizations to enhance the understanding of the dynamics of reinforcement learning algorithms.
Visualizing Translation Variation: Shakespeare's Othello
(2011)

Recognized as great works of world literature, Shakespeare’s poems and plays have been translated into dozens of languages for over 300 years. Also, there are many re-translations into the same language, for example, there are more than 60 translations of Othello into German. Every translation is a different interpretation of the play. These large quantities of translations reflect changing culture and express individual thought by the authors. They demonstrate wide connections between different world regions today, and reveal a retrospective view of their cultural, intercultural, and linguistic histories. Researchers from Arts and Humanities at Swansea University are collecting a large number of translations of William Shakespeare’s Othello. In this paper, we have developed an interactive visualization system to present, analyze and explore the variations among these different translations. Our system is composed of two parts: the structure-aware Treemap for document selection and meta data analysis, and Focus + Context parallel coordinates for in-depth document comparison and exploration. In particular, we want to learn more about which content varies highly with each translation, and which content remains stable. We also want to form hypotheses as to the implications behind these variations. Our visualization is evaluated by the domain experts from Arts and Humanities.
A Concept Analysis of Systems Thinking.
(2017)

PURPOSE
This concept analysis, written by the National Quality and Safety Education for Nurses (QSEN) RN-BSN Task Force, defines systems thinking in relation to healthcare delivery.


METHODS
A review of the literature was conducted using five databases with the keywords "systems thinking" as well as "nursing education," "nursing curriculum," "online," "capstone," "practicum," "RN-BSN/RN to BSN," "healthcare organizations," "hospitals," and "clinical agencies." Only articles that focused on systems thinking in health care were used. The authors identified defining attributes, antecedents, consequences, and empirical referents of systems thinking.


FINDINGS
Systems thinking was defined as a process applied to individuals, teams, and organizations to impact cause and effect where solutions to complex problems are accomplished through collaborative effort according to personal ability with respect to improving components and the greater whole. Four primary attributes characterized systems thinking: dynamic system, holistic perspective, pattern identification, and transformation.


CONCLUSION
Using the platform provided in this concept analysis, interprofessional practice has the ability to embrace planned efforts to improve critically needed quality and safety initiatives across patients' lifespans and all healthcare settings.
Democracy and Education Spending in Africa
(2005)

While it is widely believed that electoral competition influences public spending decisions, there has been relatively little effort to examine how recent democratization in the developing world has resulted in changes in basic service provision. There have been even fewer attempts to investigate whether democracy matters for public spending in the poorest developing countries, where “weak institutions” may mean that the formal adoption of electoral competition has little effect on policy. In this article I confront these questions directly, asking whether the shift to multiparty competition in African countries has resulted in increased spending on primary education. I develop an argument, illustrated with a game-theoretic model, which suggests that the need to obtain an electoral majority may have prompted African governments to spend more on education and to prioritize primary schools over universities within the education budget. I test three propositions from the model using panel data on electoral competition and education spending in African countries. I find clear evidence that democratically elected African governments have spent more on primary education, while spending on universities appears unaffected by democratization.
Unscented Kalman Filtering on Riemannian Manifolds
(2012)

In recent years there has been a growing interest in problems, where either the observed data or hidden state variables are confined to a known Riemannian manifold. In sequential data analysis this interest has also been growing, but rather crude algorithms have been applied: either Monte Carlo filters or brute-force discretisations. These approaches scale poorly and clearly show a missing gap: no generic analogues to Kalman filters are currently available in non-Euclidean domains. In this paper, we remedy this issue by first generalising the unscented transform and then the unscented Kalman filter to Riemannian manifolds. As the Kalman filter can be viewed as an optimisation algorithm akin to the Gauss-Newton method, our algorithm also provides a general-purpose optimisation framework on manifolds. We illustrate the suggested method on synthetic data to study robustness and convergence, on a region tracking problem using covariance features, an articulated tracking problem, a mean value optimisation and a pose optimisation problem.
Active Learning Algorithms for Multi-label Data
(2016)

The iterative supervised learning setting, in which learning algorithms can actively query an oracle for labels, e.g. a human annotator that understands the nature of the problem, is called active learning. As the learner is allowed to interactively choose the data from which it learns, it is expected that the learner would perform better with less training. The active learning approach is appropriate to machine learning applications where training labels are costly to obtain but unlabeled data is abundant. Although active learning has been widely considered for single-label learning, this is not the case for multi-label learning, in which objects can have more than one class label and a multi-label learner is trained to assign multiple labels simultaneously to an object. There are different scenarios to query the annotator. This work focuses on the scenario in which the evaluation of unlabeled data is taken into account to select the object to be labeled. In this scenario, several multilabel active learning algorithms were identified in the literature. These algorithms were implemented in a common framework and experimentally evaluated in two multi-label datasets which have different properties. The influence of the properties of the datasets in the results obtained by the multi-label active learning algoritm is highlighted.
Microstrip antenna phased array with electromagnetic bandgap substrate
(2004)

Uniplanar compact electromagnetic bandgap (UC-EBG) substrate has been proven to be an effective measure to reduce surface wave excitation in printed antenna geometries. This paper investigates the performance of a microstrip antenna phased array embedded in an UC-EBG substrate. The results show a reduction in mutual coupling between elements and provide a possible solution to the "blind spots" problem in phased array applications with printed elements. A novel and efficient UC-EBG array configuration is proposed. A probe fed patch antenna phased array of 7/spl times/5 elements on a high dielectric constant substrate was designed, built and tested. Simulation and measurement results show improvement in the active return loss and active pattern of the array center element. The tradeoffs used to obtain optimum performance are discussed.
Automatic Solution of Jigsaw Puzzles
(2013)

We present a method for automatically solving apictorial jigsaw puzzles that is based on an extension of the method of differential invariant signatures. Our algorithms are designed to solve challenging puzzles, without having to impose any restrictive assumptions on the shape of the puzzle, the shapes of the individual pieces, or their intrinsic arrangement. As a demonstration, the method was successfully used to solve two commercially available puzzles. Finally we perform some preliminary investigations into scalability of the algorithm for even larger puzzles.
A parameterized cell design for high-Q, variable width and spacing spiral inductors
(2014)

The on-chip planar spiral inductors having variable width (W) and spacing (S) across their turns are known to exhibit higher quality factors (Q). In this paper, we present an efficient parameterized cell (pcell) design in cadence using SKILL scripts for automatic layout generation of these complex, high-Q, variable W&S spiral inductors comprising of single ended and symmetric structures with rectangular, hexagonal, octagonal and circular spirals. Electromagnetic simulations are performed on the inductor layouts generated using the developed pcells. The constant W&S and variable W&S spiral inductor structures are fabricated in a 0.18 μm silicon on insulator process. Measurements show ~25% improvement in the quality factor of variable W%S spiral inductors compared to their constant W&S counterparts and also validates the proper operation of the developed inductor parameterized cells. The presented variable W&S inductor pcell significantly reduces the layout design time of RF circuit designers and also helps in the design automation of these complex inductor structures to boost their own performance and the RF circuits as well.
A Novel IoT Authorization Architecture on Hyperledger Fabric With Optimal Consensus Using Genetic Algorithm
(2018)

This paper proposes a distributed authorization architecture for Internet of Things adopting a hyperledger fabric framework. This paper also applies Genetic Algorithm to enhance the consensus component, traditionally used for Kafka, in order to determine the best configuration over different parameters, such as input transactions and their success rates. The experimental results confirm our superior performance in that the transaction transfer rate of our optimization, called GA Kafka, outperforms the traditional Kafka by 69.43%, and 89.97% for transaction transfer success rate.
Perspectives on usability guidelines for smartphone applications: An empirical investigation and systematic literature review
(2018)

Context: Several usability guidelines have been proposed to improve the usability of smartphone apps. These guidelines can be classified into three disjoint sets: platform specific guidelines, genre specific guidelines, and generic guidelines. However, smartphone applications are usually developed for multiple platforms and targeted for a variety of users. Hence the usefulness of existing guidelines is severally limited. Objective: This study aims to develop a comprehensive list of usability guidelines suitable for multiple platforms and genres of smartphone applications.
For a social network analysis of computer networks: a sociological perspective on collaborative work and virtual community
(1996)

When computer networks link people as well as machines, they become social networks. Social network analysis provides a useful approach to moving beyond the concept of “group” in studying virtual communities and computer supported cooperative work and telework. Such computer supported social networks (CSSNS) sustain strong, intermediate and weak ties that provide information and social support in both specialized and broadly-based relationships. They foster informal workplace communities that are usually partial and narrowly-focused, although some do become encompassing and broadly-based. CSSNS connect workers within and between organizations who often are physically dispersed. The nature of the medium both constrains and facilitates management control. Although many relationships function offline as well as online, CSSNS have developed their own norms and structures. They have strong societal implications, fostering situations that combine global connectivity, the fragmentation of solidarities and the de-emphasis of local organization, and the importance of home bases.